EdgeAI

Edge AI Technologies for Optimised Performance Embedded Processing

Abstract

EdgeAI is as a key initiative for the European digital transition towards intelligent processing solutions at the edge. EdgeAI will develop new electronic components and systems, processing architectures, connectivity, software, algorithms, and middleware through the combination of microelectronics, AI, embedded systems, and edge computing. EdgeAI will ensure that Europe has the necessary tools, skills, and technologies to enable edge AI as a viable alternative deployment option to legacy centralised solutions, unlocking the potential of ubiquitous AI deployment, with the long-term objective of Europe taking the lead of Intelligent Edge. EdgeAI will contribute to the Green Deal twin transition with a systemic, cross-sectoral approach, and will deliver enhanced AI-based electronic components and systems, edge processing platforms, AI frameworks and middleware. It will develop methodologies to ease, advance and tailor the design of edge AI technologies by co-ordinating efforts across 48 of the brightest and best R&D; organizations across Europe. It will demonstrate the applicability of the developed approaches across a variety of vertical solutions, considering security, trust, and energy efficiency demands inherent in each of these use cases. EdgeAI will significantly contribute to the grand societal challenge to increase the intelligent processing capabilities at the edge.

Project details

Unibo Team Leader: Andrea Acquaviva

Unibo involved Department/s:
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi"
Dipartimento di Psicologia "Renzo Canestrari"

Coordinator:
Sintef As(Norway)

Other Participants:
ALMA MATER STUDIORUM - Università di Bologna (Italy)

Total Eu Contribution: Euro (EUR) 11.654.020,00
Project Duration in months: 37
Start Date: 01/12/2022
End Date: 31/12/2025

Cordis webpage

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101097300 This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101097300